Furthermore, though the straightforward method can simply and effectively represent the categories of each caption, such a simple distinction between captions may not reflect the difference in the semantics of captions themselves.
Here, we introduce another method of topic extraction based on implicit topics.
We take advantage of sentence transformer [1]} based on BERT [2]} to generate text embeddings and then group them with DBSCAN [3]}.
The advantage of the second approach is that the different clusters tend to automatically as well as more deeply reflect the semantic differences between the different prompts.
However, the disadvantage of this is that the characteristics of each cluster need to be manually summarized, which increases the uncertainty of the conclusion.
Thus, we leverage both two methods to conduct the analysis.
Our results are shown in fig:bert.
To compare different groups' performance, we feed the corresponding fake images to the universal detector and count the outputs.
Through artificial screening, we find that most of the blue embeddings are generated by the prompt describing “person,” which is consistent with the finding of the explicit topic analysis.
In short, we can conclude that the topic “person” contributes to the authenticity of the generated images, which will furthermore make it difficult for our detector to distinguish them from real images.
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